Python | Grayscaling of Images using OpenCV
Grayscaling is the process of converting an image from other color spaces e.g RGB, CMYK, HSV, etc. to shades of gray. It varies between complete black and complete white.
Importance of grayscaling –
- Dimension reduction: For e.g. In RGB images there are three color channels and has three dimensions while grayscaled images are single dimensional.
- Reduces model complexity: Consider training neural article on RGB images of 10x10x3 pixel.The input layer will have 300 input nodes. On the other hand, the same neural network will need only 100 input node for grayscaled images.
- For other algorithms to work: There are many algorithms that are customized to work only on grayscaled images e.g. Canny edge detection function pre-implemented in OpenCV library works on Grayscaled images only.
Below is the code to Grayscale an image-
# importing opencv import cv2 # Load our input image image = cv2.imread('C:\\Documents\\full_path\\tomatoes.jpg') cv2.imshow('Original', image) cv2.waitKey() # We use cvtColor, to convert to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imshow('Grayscale', gray_image) cv2.waitKey(0) # window shown waits for any key pressing event cv2.destroyAllWindows() |
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Input image:
Output:
Faster code –
# Faster method import cv2 # The second argument zero specifies that # image is to be read in grayscale mode. img = cv2.imread('C:\\Documents\\full_path\\tomatoes.jpg', 0) cv2.imshow('Grayscale', img) cv2.waitKey() cv2.destroyAllWindows() |
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